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This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

The purpose of this paper is to assess the relationship between elderly people's health literacy skills and those people's decision to make use of digital health service platforms. Despite the substantial influence of digitisation on the delivery of healthcare services, understanding how health intervention strategies might help empower elderly people's health literacy skills is critical.

This paper analyses the existing trends in research on the convergence of health literacy, health intervention programmes and digital health service platforms by reviewing 34 studies published between 2000 and 2020.

The findings of the review indicate three primary themes (health literacy skills, health management competency and attitude/confidence), which provide a summary of the current literature, and in all three the results show that health intervention programmes help to enhance health literacy skills of elderly people. Based on the review results and by organising the fragmented status quo of health intervention research, the authors develop a comprehensive research model and identify future research directions for research in this domain.

The findings will be useful to health professionals in two ways: (1) the findings provide practical information about the growing need to implement health literacy intervention programmes to satisfy elderly people's appetite for accessing health services due to cognitive and physiological impairments, and (2) the finding help them to understand that with digital health platforms, elderly people have quicker access to health services, improving the quality of care provided to them.

This paper presents a comprehensive research model for analysing the impact of health literacy skills on older people's ability and intention to access digital health information sources, considering various health intervention approaches.

The coronavirus disease 2019 (COVID-19) pandemic highlighted the necessity of practicing Evidence-based Management (EBMgt) as an approach to decision-making in hospital settings. The literature, however, provides limited insight into the process of EBMgt and its contextual nuances. Such insight is critical for better leveraging EBMgt in practice. Therefore, the authors' aim was to integrate the literature on the process of EBMgt in hospital settings, identify the gaps in knowledge and delineate areas for future research.

The authors conducted a systematic scoping review using an innovative methodology that involved two systematic searches. First using EBMgt terminology and second using terminology associated with the EBMgt concept, which the authors derived from the first search.

The authors identified 218 relevant articles, which using content analysis, they mapped onto the grounded model of the EBMgt process; a novel model of the EBMgt process developed by Sahakian and colleagues. The authors found that the English language literature provides limited insight into the role of managers' perceptions and motives in EBMgt, the practice of EBMgt in Global South countries, and the outcomes of EBMgt. Overall, this study’s findings indicated that aspects of the decision-maker, context and outcomes have been neglected in EBMgt.

The authors contributed to the EBMgt literature by identifying these gaps and proposing future research areas and to the systematic review literature by developing a novel scoping review method.

The Chulalongkorn-Hasanuddin Rifampicin-Resistant Tuberculosis Screening Tool (CUHAS-ROBUST) is an artificial intelligence–based (AI–based) application for rifampicin-resistant tuberculosis (RR-TB) screening. This study aims to elaborate on the drug-resistant TB (DR-TB) problem and the impact of CUHAS-ROBUST implementation on RR-TB screening.

A qualitative approach with content analysis was performed from September 2020 to October 2020. Medical staff from the primary care center were invited online for application trials and in-depth video call interviews. Transcripts were derived as a data source. An inductive thematic data saturation technique was conducted. Descriptive data of participants, user experience and the impact on the health service were summarized

A total of 33 participants were selected from eight major islands in Indonesia. The findings show that DR-TB is a new threat, and its diagnosis faces obstacles particularly prolonged waiting time and inevitable delayed treatment. Despite overcoming the RR-TB screening problems with fast prediction, the dubious screening performance, and the reliability of data collection for input parameters were the main concerns of CUHAS-ROBUST. Nevertheless, this application increases the confidence in decision-making, promotes medical procedure compliance, active surveillance and enhancing a low-cost screening approach.

The CUHAS-ROBUST achieved its purpose as a tool for clinical decision-making in RR-TB screening. Moreover, this study demonstrates AI roles in enhancing health-care quality and boost public health efforts against tuberculosis.

Breast cancer is a global public health dilemma and the most prevalent cancer in the world. Effective treatment plans improve patient survival rates and well-being. The five-year survival rate is often used to develop treatment selection and survival prediction models. However, unlike other types of cancer, breast cancer patients can have long survival rates. Therefore, the authors propose a novel two-level framework to provide clinical decision support for treatment selection contingent on survival prediction.

The first level classifies patients into different survival periods using machine learning algorithms. The second level has two models with different survival rates (five-year and ten-year). Thus, based on the classification results of the first level, the authors employed Bayesian networks (BNs) to infer the effect of treatment on survival in the second level.

The authors validated the proposed approach with electronic health record data from the TriNetX Research Network. For the first level, the authors obtained 85% accuracy in survival classification. For the second level, the authors found that the topology of BNs using Causal Minimum Message Length had the highest accuracy and area under the ROC curve for both models. Notably, treatment selection substantially impacted survival rates, implying the two-level approach better aided clinical decision support on treatment selection.

The authors have developed a reference tool for medical practitioners that supports treatment decisions and patient education to identify patient treatment preferences and to enhance patient healthcare.

Inscrutable machine learning (ML) models are part of increasingly many information systems. Understanding how these models behave, and what their output is based on, is a challenge for developers let alone non-technical end users.

The authors investigate how AI systems and their decisions ought to be explained for end users through a systematic literature review.

The authors’ synthesis of the literature suggests that AI system communication for end users has five high-level goals: (1) understandability, (2) trustworthiness, (3) transparency, (4) controllability and (5) fairness. The authors identified several design recommendations, such as offering personalized and on-demand explanations and focusing on the explainability of key functionalities instead of aiming to explain the whole system. There exists multiple trade-offs in AI system explanations, and there is no single best solution that fits all cases.

Based on the synthesis, the authors provide a design framework for explaining AI systems to end users. The study contributes to the work on AI governance by suggesting guidelines on how to make AI systems more understandable, fair, trustworthy, controllable and transparent.

This literature review brings together the literature on AI system communication and explainable AI (XAI) for end users. Building on previous academic literature on the topic, it provides synthesized insights, design recommendations and future research agenda.

Bureaucracy to a large extent entrenches orderliness and productive means of achieving goals in both public and private organisations across the world. However, bureaucracy is not suitable in the management of hospitals due to its peculiar nature of operations. This study investigates the conundrum of bureaucratic processes and health-care service delivery in government hospitals in Nigeria.

The study surveyed 600 outpatients and attendees visiting tertiary and government hospitals in Nigeria using descriptive design to obtained data from the respondents. A research instrument, questionnaire, was used to gather data. Out of the 600 outpatients visiting the 20 hospitals in government and tertiary hospitals, 494 responses were returned from the attendees. The study employed random sampling strategy to collect the information.

The findings of this study were that service delivery in government hospitals were in adverse position on all the four constructs of bureaucratic dimensions as against quality of service delivery in hospitals in Nigeria. It discovered that bureaucratic impersonality cannot impact on the quality of service delivery in government hospitals in Nigeria. Separation and division of labour among health workers have no significant effect on quality service delivery in government hospitals. Formal rules and regulations (administrative procedure, rules, and policies) prevent quality service delivery in government hospitals in Nigeria. Also, patient’s waiting time was not significant to the quality of service delivery in government hospitals.

The results are constrained with dimensions of bureaucratic processes. Thus, the implication of this study is that bureaucracy in the Nigerian public hospitals is an unnecessary marriage which should be carefully separated and de-emphasised for quality service delivery in the hospitals to thrive.

Largely, this study is practical essential as it unearths the irrelevant operations procedure that hinder progress in Nigerian hospitals.

The study accomplishes recognised importance to survey how bureaucracy impedes quality service delivery in government hospitals. This study has provided a vital clue to elements that will bring rapid attention to patients’outcome in Nigerian hospitals and health-care facilities which hitherto has not been emphasised. The study has contributed to the existing body of knowledge associated to healthcare service quality in developing country.

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

In the management world, leadership is a quality associated with business leaders, social entrepreneurs and political figures. Doctors are rarely considered as possessing or requiring leadership skills. With doctors, one thinks of skill and knowledge, but for some strange reason, leadership is hardly associated with doctors. This paper aims to highlight the leadership aspects unique to doctors. This study highlights why leadership training is imperative for doctors, outlines current status of leadership training for doctors in India and sets out proposals for effective leadership building.

Methodology is based on a two-pronged explanatory approach – the first is review of current literature in the context of leadership training of doctors, and the second is review of circumstances unique to the line of work undertaken by doctors that shed light on the need for leadership.

This paper highlights the imperative need for leadership training for doctors in India. It recommends leadership training on a continuous basis in their career life cycle as with the other professions. It also calls for involvement of all stakeholders in the medical community to foster leadership training – medical educational institutions, hospitals, medical councils and members of the medical fraternity.

Akin to leadership training programs conducted for IT and management professionals, this paper recommends that similar programs be conducted for doctors.

There are very few studies conducted in the Indian context on leadership training needs for doctors. This paper explains the importance of leadership training for doctors and suggests ways it can be implemented throughout the medical education life cycle of a doctor’s career.

When discussing the term “technology-facilitated violence” (TFV) it is often asked: “Is it actually violence?” While international human rights standards, such as the United Nations' Convention on the Elimination of All Forms of Discrimination against Women (United Nations General Assembly, 1979), have long recognized emotional and psychological abuse as forms of violence, including many forms of technology-facilitated abuse (United Nations, 2018), law makers and the general public continue to grapple with the question of whether certain harmful technology-facilitated behaviors are actually forms of violence. This chapter explores this question in two parts. First, it reviews three theoretical concepts of violence and examines how these concepts apply to technology-facilitated behaviors. In doing so, this chapter aims to demonstrate how some harmful technology-facilitated behaviors fit under the greater conceptual umbrella of violence. Second, it examines two recent cases, one from the British Columbia Court of Appeal (BCCA) in Canada and a Romanian case from the European Court of Human Rights (ECtHR), that received attention for their legal determinations on whether to define harmful technology-facilitated behaviors as forms of violence or not. This chapter concludes with observations on why we should conceptualize certain technology-facilitated behaviors as forms of violence.